- Sussex Research Online

Transcription

International Journal of Engineering and Technology Innovation, vol. 1, no. 1, 2011, pp. 01-11
Eco Control of Agro Pests using Imaging, Modelling &
Natural Predators
Fina Faithpraise1 , Joseph Idung2 , Chris Chatwin1 ,＊, Rupert Young1 , Philip Birch1
1
School of Engineering and Informatics, Biomedical Engineering, University of Sussex, Brighton –UK .
2
Zoology and Environmental Biology; University of Calabar, Nigeria.
Received 15 June 2011; received in revised form 05 August 2010; accepted 10 September 2010
Abstract
Caterpillars in their various forms: size, shape, and colour cause significant harm to c rops and humans. This
paper offers a solution for the detection and control of caterpillars through the use of a sustainable pest control
system that does not require the application of chemical pesticides, which damage human health and destroy the
naturally beneficial insects within the environment. The proposed system is capable of controlling 80% o f the
population of caterpillars in less than 65 days by deploying a controlled number of larval parasitoid wasps
(Cotesia Flavipes (Cameron)) into the crop environment. Th is is made possible by using a continuous time model
of the interaction between the caterpillar and the Cotesia Flavipes (Cameron) wasps using a set of simultaneous,
non-linear, ord inary differential equations incorporating natural death rates based on the Weibull probability
distribution function. A negative b inomial d istribution is used to model the efficiency and the probability that the
wasp will find and parasitize a host larva. The caterpillar is presented in all its life-cycle stages of: egg, larva,
pupa and adult and the Cotesia Flavipes (Cameron) wasp is present as an adult larval parasitoid. Biological
control modelling is used to estimate the quantity of the Cotesia Flavipes (Cameron) wasps that should be
introduced into the caterpillar infested environment to suppress its population density to an economically
acceptable level within a prescribed number of days.
Keywords: caterpillar pest control, system modelling, sustainable biological control, cotesia flavipes (cameron)
wasps
1.
Introduction
Caterpillars are the larvae of insects which belong to the second largest of all insect orders Lepidoptera. There are
thousands of species of caterpillars inhabiting different countries worldwide. They are a serious pest and voracious feeders
that can destroy entire food crops within a short time interval. They appear in different shapes, colours, and sizes. Some
species are bare skinned, others have sparse or dense fine hair (setae); some have dull colors, others are brightly mult icoloured; some have even and regular bodies, others carry one to many protuberances, bristles, spines, and/or structures
resembling horn projections. Many are equipped with the ability to repel predators due to their poisonous glands (non –
edible); as a result they defend themselves by ejecting acid.
＊Corresponding author. E-mail address: [email protected]
Tel.: +44 1273 678901; Fax: +44 1273 690814
2
International Journal of Engineering and Technology Innovat ion, vol. 1, no. 1, 2011, pp. 01-11
They are camouflaged or cryptically colored and almost look similar to the plants on which they feed and might have
sections that resemble plant parts as illustrated in Figs. 1 and 2. Frazier [1] and Hyche [2] classified caterpillars into stingers
and non- stingers according to the nature of the harm they do to crops and humans. Stinging caterpillars are equipped with
urticaceous setae or spines, which are hollow and contain toxins secreted from poison-gland cells; these are defensive
structures for protection against predators. The stinger caterpillars are so dangerous that contact with either the live or d ead
spines of some of the species may cause skin irritation and burning sensations; dermatit is and pustules; inflammation and
numbness at or around the area of contact; fever and nausea; and intense pains - depending on the degree of contact, the
specie of caterpillar and the type of to xin the caterpillar possesses. Non –stinger caterpillars have a frightening look but some
may be harmless as shown in Fig. 2.
Caterpillars can cause life threating diseases , Malaque [3] reported the life threatening ailments caused by accidental
contact with caterpillars - in some cases burning pains, oedema and erythema. Arocha-Piñango [4] reported Fibrinolysis,
blood coagulation disorders or miscarriage fro m contact with caterpillars . Kelen [5] demonstrated Hemo rrhagic syndrome
induced by contact with the genus Lonomia (Saturniidae, Hemileucinae). Brady [6] and Burd mann [7] reported severe acute
renal failure induced by the venom of Lonomia caterpillars. Several studies have revealed that most caterpillars have
developed resistance to chemical pesticides , which has resulted in a resurgence of their population and pest outbreaks
Chikwenhere [8], Wakamu ra [9]. Our focus is specifically on the deployment of natural enemies (parasitoid wasp) into areas
where heavy pesticide use is common such as vegetable and cereal crops .
To find lasting solutions to the problem of caterpillars, we hypothesize that the timely detection of caterpillar pests
reduces its effects on crops and human health. Co ming into contact with so me species of the caterpillar can be dangerous
with victims having to seek medical attention. For farmers to avoid encountering the cate rpillar, a detection system capable
of detection and eradicat ion of the different species of caterpillar at min imu m cost is proposed. All the different species a nd
varieties of caterpillars have the same developmental life cycles stages, though the seasons of development may differ; for
the purposes of this wo rk we shall consider the detection and eradication of the larva stage of the moth Spodoptera Exempta
(army worm), a non-stinger caterpillar. The egg stage is important because the very plant that harbo urs the eggs is usually
the first victim of the hatching caterpillar, as it serves as its food plant. The larva stage is the eating and the rapid gro wth
stage of the moth. At this stage the larva eats voraciously for 24 hours to nourish it body and store u p the food that will be
used later in its transition to the pupa and to the adult stage.
Packsaddle
s
Hag moth
Stinging rose Spiny oak slug Isa textula
Puss
White flannel Buck moth
Smeared
dagger
Eastern
tent
Io moth
Whitemarked Variable
tussock
oakleaf
Fig. 1 Stinger caterpillar database
Forest tent
Spiny Elm
International Journal of Engineering and Technology Innovation, vol . 1, no. 1, 2011, pp. 01-11
Imperial
Army worm
Waved sphinx
Black-etched
Spicebush
swallowtail
Spiny oak
worm
Hornworm
Gray furcula
Sycamore
tussock
3
Walnut
Fall webworm
Hickory horned
devil
Fig. 2 Non- stinger caterpillar database
Timely detection imp lies identifying the caterpillar fro m the eggs to the pupae sta ge. This will be achieved by the
application of the sustainable pest control system. This system can achieve the detection and control of the three pre-adult
stages automatically, at lo w cost, without co ming into contact with the caterpillar and without su bsequently requiring the
application of chemical pesticides. So lving the caterpillar problem will increase the quantity and quality of food production ,
which would be a great achievement to maintain food security and good health.
For this simu lation, a cereal crop farm, and the “Spodoptera Exempta (Se)(caterpillar)” pest is considered using a h igh
speed video camera (Gopro Hero 3) mounted on a Pest surveillance system as shown in Fig. 3. Spodoptera Exempta is
chosen as a case study, since it requires early d iscovery of all three pre-adult stages of its life cycle (Eggs, larva and pupa)
monitoring and treat ment to prevent spreading, uncontrollable infestation and out breaks as shown in Fig. 4. A video camera
is required because of its ability to capture moving images of the insect at up to 12 fps with continuous autofocus for razor
sharp tracking of moving objects.
For this simu lation, the surveillance system captures the caterpillar images and transmits these via a wireless network
to the detection system (Automat ic p lant pest detection and recognition algorith m) (APDRS) for detection and analysis of
the images captured. The detection subsystem processes the images as demonstrated by the pest detection and recognition
system of Faithpraise [10] and Kypraios [11] .
The system transmits the output, as displayed in Fig. 5, to the pest recognition unit to be processed in order to know the
exact species of the pest dominating the cereal farm. The recognition algorith m matches the detected pest to the e-database
and displays the result as a 3D plot confirming the number of pests present in each crop image by the number of output
spikes, as shown in Fig. 6.
Fro m the APDRS system, the sampled field images are forwarded to the biological control model (ECM) designed by
Faithpraise [12] to estimate the approximate population density of caterpillars in the field, to enable the deployment of the
correct population density of wasps to accurately combat and control the population growth rate o f the pest within an
estimated period of time. For more information on the pest surveillance system, see Faithpraise [13]
4
Robotic camera drone system
(RCD)
APDR
Fig. 3 Pest surveillance system
Fig. 4 Pest Outbreak [7]
Original
pest images
Detected
Original
Detected
pest images pest images pest images
Fig. 5 Detected pest images of the different caterpillar species
5
(a)
(b)
Fig. 6 Output of caterpillar image from the correspondence filter
2.
Eco Control Model (ECM)
The goals of the ECM are to:
i)
ii)
iii)
iv)
v)
vi)
3.
Maintain the density of the pest population at an equilibrium threshold below the economic damage level.
Reduce the pest population to a low level but not to completely destroy them as can occur with pesticides.
Publicize the effectiveness of biological control systems and their sustainable control capabilities.
To preserve the existence of the naturally beneficial insects and encourage the increase in their numbers
To encourage the restoration of natural habitats and discourage completely the application of all classes of pesticides
To estimate the right quantities of the naturally beneficial insects to deploy in any pest infested habitat
Laval Parasitoid wasps
The parasitoid wasps use the host as illustrated by Godfray [14] and Yuan [15] who report that the larval parasitoids
deposit and develop their offspring in the body of an insect pest (host). Edson [16] illustrates the injection of venom by
parasitoids wasps, which paralysed and modified the host's tissues making the h ost more nutritious for the developing wasp
larva. In most cases the wasp enters the insect’s body and reproduces within the insect as illustrated by Grosman [17] on the
interaction of braconid parasitoids (Glyptapanteles sp.) with the moth caterpillar (Thyrinteina leucocerae). The wasps
reproduce by laying eggs within the bodies of the caterpillars. The wasps develop and feed on life–supporting tissues in the
inside of the caterpillar, it eventually forces it way out fro m the host to pupate, the wasps meta morphose into adult larval
parasitoids and the life cycle repeats . A Pascal (negative binomial distribution) is used to model the efficiency and
probability that the wasp will find and parasitize a host larva, this probability distribution is described in [18]
4.
Materials and Methodology
As an illustration of the concept, we propose a model of the interaction between a population of Spodoptera Exempta
moths (Nh ) and its life cycle stages: the egg (Ne ), larvae (Nl ) and pupae (Np ) with the larval parasitoid (Nlw) Cotesia Flavipes
(Cameron) as shown in Fig. 7. The Spodoptera Exempta moth is considered because of its economic importance and its
affinity to vegetables and cereal crops, which often suffer loses of hundreds tonnes of crops due to this pest, Chikwenhere [7].
Cereal crops are: maize, wheat, oats, barley, rye and rice, in addition to legumes, forage grass es, and various vegetable
crops. Of the cereal crops maize is chosen because it is economically important with broad recognition across nations. Maize
is very susceptible to the attacks by the Se caterpillar.
The Cotesia Flavipes (Cameron) is a braconidae gregarious larval endoparasitoid wasp, which can lay 40 eggs in the
host larva, Kfir [19] and can deposit up to 40 eggs in 3-4 host larvae in a day. It has the ability to parasit ize 20 host larvae in
its life span, as demonstrated by Potting [20]
6
Table1. Parasitoid wasp’s life span. The table records the number of eggs produced by the
larval parasitoid wasp, the incubation periods, the length of time it takes for the wasps to
emerge and the wasp’s life span in the presence and absence of food. [19], [21], [22] and [23]
Life span of the Larval parasitoid wasps
(Cotesia Flavipes (Cameron))
No of eggs
35-40
egg incubation period
2 -3 days
Larva - wasp larva emerges from larva
12-16 days
Pupa
4-8 days
Adults emerge
9-11 days
Wasp lifetime in the presence of food
6-10 days
Wasp lifetime in the absence of food
2-3.2 days
Table 2. Spodoptera Exempta development from egg to adult, [24],
[25],[26]and [27]
Life span of the Spodotera exempta (Cameron)
No of eggs per day
100-300
egg incubation period
2 -5 days
Larva
14-19 days
Pupa
7-8 days
Adults life span
10-14days
Fig. 7 Population dynamics schematic for wasp-pest-crop interaction model describing the detailed activities of how the
wasps exercise control over pest population in its habitat
The following simultaneous, ordinary differential equations are derived from Fig.7, provide a dynamic model of the
evolving pest, Spodotera exempta cameron, Cotesia Flavipes (Cameron) and leaf population per square metre
dN e
 N h  N e  me N e
dt
(1)
7
dN l
 N e  N l  bN l N lw  m l N l  N l
dt
(2)
dN lw
 bN l N lw  p lm N lw
dt
(3)
dN p
(4)
dt
 N l  N p  m p N p
  K  Nh
dN h
 {N p  m h N h } N h  h
dt
  Kh
dN lf
dt



(5)
 N lf  N lf N l
(6)
where
N h , N e , Nl , N p
N lw
= Population density of Spodotera exempta: adult, egg, larvae and pupae.
= Population density of parasitoid wasps.
Kh = Spodotera exempta carrying capacity of the environment.
mh , me , ml , m p =Spodotera exempta: adult,egg, larvae and pupae mortality
p lm = larval parasitoid wasp mortality

rate, respectively.
rate.
= efficiency of turning prey into parasitoid wasps offs pring.
b = probability that a parasitoid finds and parasitizes a larva prey
 = Number of eggs per day from each Spodotera exempta
 = Fraction of eggs hatching into larvae
 = Fraction of larvae changing to pupae
 = Fraction of pupae turning into moths
 = Leaf impact factor
 = Leaf growth rate
 = Fraction of leaves eaten by a caterpillar
i
per unit time
N lf = Initial population of leaves
N lf = Population of leaves

i
N lf  N lf
i
N lf
leaf-larvae coupling coefficient
Eqn. 6 models the leaf population and leaf growth rate, wh ich is determined using the Hoffmann’s [28] relative gro wth
rate equations, maize growth rate and development Tóth [29], Nelissen [30] and Hardacre [31].
8
The proposed model consists of six simu ltaneous non -linear, ordinary d ifferential equations (1) to (6), wh ich are
solved using a 4th order Runge –Kutta method as described by Fehlberg [32]; Dormand [33]; Mangano [34]; and Schreiber
[35] , and using the life span of all the insects and their mortality rates as displayed in Tables 1 & 2.
The Se moth and its life cycle forms (egg, larvae and pupae) and the Cotesia Flavipes (Cameron) wasps have a unique
death rate as shown in Table 1 and Table 2, which is already established fro m the literature and several research papers. The
parasitoid wasps mortality rate was determined by the use of a distributive function as described by Chatfield [36] and Ostle
[37] in their work on the Weibull distribution. Hence, the mortality rates of the Se moth and its offspring and the larval
parasitoid wasps can be modelled using eqn. (7) to (10).
Application of the Weibull probability d istribution function was seen in the determination o f the mo rtality rates of
mosquitoes and the predators Odonata and Toxorhynchites , Faithpraise [38]. In a similar manner, we have determined the
mortality rates of the Cotesia Flavipes (Cameron) wasp and the Spodoptera Exempta using the Weibull probability
distribution functions, the results are summarized in Table 3.
The life expectancy of the parasitoid wasps and pest can be represented by the Weibull distribution below:
X ~ W  ,  
(7)
So that
m


 1
x
 
 
(2)
where
m = mortality

= gradient of the least squares line of the Weibull probability plot.  is the 63.2th quantile of a Weibull d istribution. The
intercept =  ln  , if we decide to calcu late the least square line equation based on the plotted points x (1) …. x(n), then b1
and
b0 will be estimates for  and  ln  , such that   b1 and   e
b
  0
 b1



where
b0 is the intercept point and b1 is
the slope Chatfield [31]and Ostle [32].
Weibull plot for the Se larvae
0.4
0.2
0.2
0
0
probability plot (y)
probability plot (y)
Weibull plot for the Cotesia F larval wasps
0.4
-0.2
-0.4
y= -1.3342 +0.6207ln(x)
-0.2
-0.4
y = -7.4074 + 2.4642ln(x)
-0.6
-0.6
-0.8
-0.8
-1
0.5
1
1.5
ln(x)
2
2.5
-1
2.6
2.7
2.8
2.9
3
3.1
ln(x)
Fig. 8 Weibull plot of the Se moth larvae and Cotesia Flavipes (Cameron) wasp to determine the
mortalities as illustrated in the data in Table 3.
To determine

9
and , a graphical technique, shown in Fig. 8, was used; this considers the age factor (x) and the probability
distribution function (y). The variable (x) is estimated using the minimu m and maximu m life span of the insects, pi . The
distributed data points are calculated fro m equation 9, while yi . the probability function is determined fro m equation 10,
where n is the total number of data points , Faithpraise [39].
pi 
i
n 1
  1
yi  ln ln
  1  pi
(9)



(10)
The mortality rates of the Cotesia Flavipes (Cameron) wasps (
plw ) and the Se moth life
cycle stages
( mh , me , ml , m p ) are estimated using equations (7) to (10) and the results are summarised in Table 3
Table3. Life expectancy of larval wasps, Tanwar [21], Murthy [20]and the caterpillar, Mangano
[23]and Mahmoud [26]. Mortality rates of the wasps and pest were obtained from the life span
by applying the Weibull probability distribution function
b 
  0 
 b1 
Mortalities
life span in
days (x)
ln (x1)
ln (x2)
ψ= b 1
(b 0 /b 1 )
plm
2-10
0.6931
2.3026
0.6207
-2.1495
8.5806
0.0683
mh
10 – 13
2.3026
2.4765
5.9820
-2.5490
12.7947
0.5061
me
2–5
0.6931
1.2528
1.7849
-1.1996
3.3187
0.60
ml
7-14
2.6391
3.0445
2.4642
-3.0060
20.2065
0.134
 e
Mortality
obtained
mp
7 - 10
1.9459
2.3026
2.8007
-2.2686
9.6663
0.308
Our aim is to find a lasting solution to the damaging effect of the caterpillar, which are very harmfu l to the crop and to
also to understand how effective the Cotesia Flavipes (Cameron) larval parasitoid wasp can be, when deployed for pest
control. In this illustrative simu lation we consider a square metre area of maize gro wing habitat with 4 plants per square
metre each with 13 – 17 leaves per plant, Bean [40]. We assume an init ial equilibriu m population density of 7 adult female
Spodoptera Exempta (Se), wh ich lay an average of 150 eggs per day. The model is run with only an assumption about the
number of invading Se moths, with no initial nu mber of eggs, larva or pupa. The outcome is very interesting but it is more
interesting to investigate an established infestation. The in itial estimates of infestation population density can be provided
using a pest surveillance system as illustrated in Fig. 3, or possibly the data may be collected by manual counting and
inspection of leaves depending on the size of the field. After some eggs transform into larva, a significant effect is noticed on
the growth of the leaves, as indicated in Fig 9. To prevent the total destruction of the maize crop we introduce larval
parasitoid wasps, Cotesia Flavipes (Cameron), into the growing habitat. For this illustration we set the initial Cotesia
Flavipes (Cameron) wasps population to 5 per square metre, the simulation results are presented in Fig. 9 to Fig. 15 .
5.
Results and Observations
5.1.
A constant maize leaf growth
Fig. 9 illustrates a scenario where there is an absence of caterpillars in the crop habitat (maize field). Hence all the
insect variables (Nh = Ne =Nl =Np = 0) are set to zero, indicating the absence of moths visiting the habitat. The results plotted
in Fig. 9 shows the normal uninterrupted growth rate of the crop over an interval of 100 days, the leaves increased from 68 to
281.
10
Pest population density/ m 2
300
Maize leaves
250
200
150
100
50
Maize Leaf(Nlf)
0
20
40
60
80
100
Time (days)
Fig. 9 Leaf growth in the absence of pests
5.2.
Spodoptera Exempta (Se) visitation
In Fig. 10 a scenario is presented where seven Se ( i Nh = 7) are alighting on the maize leaves and laying eggs, The
initial pest populations are: Ne = Nl = Np = 0; there are no wasps deployed, so the infestation has just begun. After a period
of 17 days, there was a significant drop in the leaf population which was 81 and dropped to 33 in 60 days; subsequently the
leaves continued to drop to 21 within a period o f 100 days; this is the result of the larvae eat ing the leaves. After an interval
of 20 days the population of the Se moths saturates at 50, which is the environmental carrying capacity; this limits the
population density of the eggs, which also saturates and peaks at 842 in 25 days. The transformation of eggs into larva peaks
at 481 larvae in 24 days causing a significant drop in the leaf population, wh ich adversely affects the population of the larvae
as the population dropped to 281 in 60 days with a subsequent drop to 250 in the 100 day period, this is due to the shortage
of food.
900
Se eggs
Se larvae
Se pupae
Se adults
Maize leaves
800
700
600
500
400
300
200
100
0
Maize Leaf(Nlf)
0
20
40
60
80
100
Time (days)
Fig. 10 The effect of Spodoptera Exempta (Se) visiting the maize field habitat
5.3.
Introduction of a control measure
Immediately the pest surveillance system captures a snap shot of the pest on the maize leaves, it was decided to
explore the effect of deploying different numbers of the larval parasitoid wasps into the affected habitats to understand the
control strength of the wasps and the most economical approach to control the pest population to an economically acceptable
level.
11
Using the same in itial population densities of: Ne =0, Nl =0, Np = 0; 7 Se moths and 68 leaves. We assume that each
Se moth lays 150 eggs per day. The first step was to deploy five Cotesia Flavipes (Cameron) (Nlw) wasps to determine their
control strength on the pest population density; the result is shown in Fig. 11. In Fig. 11, the leaf population increases
slightly to 84 leaves with in the first 19 days and falls to a min imu m of 69 after 35 days. The wasp population drops to three
after 20 days, which reduces their effect in controlling the pest larva. As the wasp population mult iplies to a peak of 47,
significant leaf growth was experienced to 111 within the 100 day period, during this period the Se larvae population
dropped to a value of 42. The maximu m pest population grows to: 843 eggs in 29 days and drops to 179 eggs after 100 days,
379 larva in 24 days dropping to 42 larva in 100 days, 177 pupa in 25 days dropping to 21 pupa in 100 days and 50 Se moths
in 22 days dropping to 11 Se moths, which are economically viable values but the moth egg density is still high at 179.
900
Se eggs
Se larvae
Cotesia F.
Se pupae
Se adults
Maize leaves
800
700
600
500
400
300
200
100
0
Maize Leaf(Nlf)
0
10
20
30
40
50
60
70
80
90
100
Time (days)
Fig. 11 Effect of deploying five Cotesia Flavipes (Cameron) larval wasps
Step 2: due to the losses observed in the leaf population of Fig. 11, and the failu re to suppress the Se moth egg
population, twenty five Cotesia Flavipes (Cameron) wasps were introduced into the habitat.
Using the same init ial population densities of: 7 Se moths, Ne = Nl = Np = 0; and 68 leaves and with the assumption
of 150 eggs laid per day per Se moth. Twenty five Cotesia Flavipes (Cameron) (Nlw) wasps were deployed into the maize
field habitat, the result is shown in Fig. 12. In the result of Fig. 12, the control activ ities of the Cotesia wasps is
overwhelming as observed in the growth values of the Se moths and its life cycle stages. It to ok the Se moths 33 days to
reach 50 which is the environmental carrying capacity value. This also affected the life cycles stages as it took them longer
to reach their peaks. The result shows the maximu m pest population grows to: 843 eggs in 39 days, 316 larvae in 32 days,
147 pupae in 33 days and 50 Se moths in 33 days. Due to the control exerted by the Cotesia Flavipes (Cameron) wasps on
the Se moths larva their population dropped to 26 in 60 days, the eggs, pupae and moths population dropped to 186 for eggs,
13 pupae and 6 Se moths in 60 days and the population of the pest continued to dwindle across the 100 day period wh ile
there is constant growth of the maize leaves fro m 68 to 94 in 30 days, there is a slight drop to a min imu m of 84 leaves by day
50, after which the leaves regained their growth to 128 for the 100 day period.
Fro m the Fig. 12, we observe that most of the leaf destruction occurred at the peak of the larva population, when the
wasp population dropped from 25 to 6 as there were no in itial larvae or host to be parasitized. So as the Cotesia wasps
population increases to 55, there was a significant drop in the larvae population with corresponding impact on the rest of the
pest populations (eggs, pupae and Se moths).
This interesting oscillatory relationship observed between the Cotesia Flavipes (Cameron) wasp population and the
pests tends to control the pest population to a metastable value, as the population density of the egg, larvae, pupa and moth
were controlled to 186, 44, 22 and 11 respectively; which are economically viab le values . The populations move towards
12
equilibriu m as the leaf growth increases to 128 for the 100 day period. The oscillatory relat ionship observed is the result of
the lack of d irect control of the other pest life cycle stages (egg and pupae). So every larva that escapes attack transforms
into a pupa, which metamorphose into adults, which continue the reproduction of the egg stage.
5.4 .
Model reliability
Fro m the result of Fig. 10, there were severe leaf losses from the 19th day, when the life cycle of the Se moths
completed and there was a rise in the larvae population after the visitation o f the Se moths to the maize farm. An init ial leaf
growth of 81 fro m the in itial leaf starting value of 68 was observed in the first 18 days. Significant constant leaf destruction
was noted when the larvae population grew to a peak of 481 in 24 days.
Fro m the result of Fig. 11, it was observed that, the presence of Cotesia wasps introduced early into the habitats has
significant effect on the activities of the caterpillar. Fo r instance it was observed that the initial leaf gro wth p eaks at 83 in the
first 19 days. The caterpillar population peaks at 379 in 24 days, the impact was observed in the leaves within a period of 34
days (fro m 20th to 54th day). The negative impact on the leaves lasted a long time because of the limited nu mber of wasps
deployed. As soon as the wasp population increased to the peak of 46 Cotesia wasps, significant control of the Se larvae was
achieved to a minimum value of 25 in 60 days and 42 in 100 days as the leaf population increased to 111.
In Fig 11 and 12 an init ial d rop was observed in the population of the Cotesia Flavipes wasps from 5 to 2 in the first
16 days and 25 to 6 in the first 23 days respectively, this was due to the lack of host larvae to be parasitized, until the moths
eggs transformed to larvae, then there were hosts for the wasps.
Fro m the result of Fig. 12, it was observed that the greater the number of wasps deployed, the higher the leaf growth
and the smaller the impact of the pest on the leaf population. For instance in Fig. 12 when 25 Cotesia Flavipes (Cameron)
wasp were introduce, there was uninterrupted leaf growth fro m 68 to 94 in the first 29 days with neglig ible leaf gro wth drop
to 83 within 21 days (between the 31st to 52th days) though the leaf growth value is still higher than the in itial starting value
of 68. The wasps regain control within a short period and subdue the pressure fro m the caterpillar to achieve a leaf growth to
129 after 100 days with a significant drop to the population of the larvae to 44.
5.5. Established infestation
In a scenario where an infestation is already established as illustrated in Fig. 13, using init ial population densities of: 7
Se moths, Ne =150, Nl = 120, Np = 90 and 68 leaves. We assume that each Se moth lays 150 eggs per day. It was observed in
Fig. 14 that 5 Cotesia Flavipes (Cameron) population started to rise contrary to our previous observations in Figs 12 and 13,
where the population was observed to drop first before rising. This increase in the wasp population contributed to the earlier
suppression of the Se moth larvae fro m 274 to 100 larvae, wh ich defin itely causes a reduction in the population of the pupae
fro m 129 to 45, with a subsequent fall in the population of the Se moth fro m 50 to 22, which also affected the production of
eggs from a maximu m value of 843 down to 333 eggs. It is interesting to note that, in an established infestation, the control
impact on the larvae stage by the wasps could cause instability on the Se moth population, despite the Se moths reaching the
environmental carry ing capacity, significant suppression was observed, which also reflects in the reduction in the pest egg
population. But what we are interested in is the total suppression of both the other stages (eggs and pupae) to the barest
economically acceptable threshold, which should be below 100 per m2 . In order to achieve our goals, several quantities of
Cotesia Flavipes (Cameron) wasps were tried on the pest infestation to evaluate the quantity, which can actually suppress all
the pests population to a symbiotic level. The most favourable result was observed when twenty five Cotesia Flavipes
(Cameron) wasps were introduce as indicated in Fig. 14.
900
Se eggs
Se larvae
Cotesia F.
Se pupae
Se adults
Maize leaves
800
13
700
600
500
400
300
200
Maize Leaf(Nlf)
100
0
0
20
40
60
80
100
Time (days)
Fig. 13 Effect of five Cotesia Flavipes (Cameron) wasps on an established infestation
In a scenario where an infestation is already established as illustrated in Fig. 13, using the initial population densities
of: 7 Se moths, Ne =150, Nl = 120, Np = 90 and 68 leaves. Twenty five (25) Cotesia Flavipes (Cameron) wasps were
introduced. The result shows a maximu m population growth to 463 eggs, 104 larvae, 55 pupae, and 25 Se moths . The
effective control of the Cotesia wasps was observed by the significant drop in the population of all the pest life cycle stages
to 330 eggs, 79 larvae, 40 pupae and 20 Se moths for the 100 day period. There was no actual destruction of the maize
leaves across the 100 days to less than the initial leaf starting population of 68. Effect ive control by the Cotesia wasps was
observed on the pest suppression values with a constantly increasing leaf growth to 127 for the 100 days. The result shows
the value of the egg population 330, has still risen above the minimu m allowable economic threshold whereas the larvae, the
pupae and the Se moth population is maintained to a metastable values as the leaf growth rise s to 128.
Fig. 14 Effect of twenty five Cotesia Flavipes (Cameron) wasps on an established infestation
The long term effect of none application of chemical pesticides will result in stability and symbiotic existence within
the insect kingdom as observed in Fig. 15 where the plant-pest–wasp interaction becomes stable as the pest population was
maintained at the minimu m threshold value of 322 eggs, 79 larvae, 38 pupae, and 19 moths with co rresponding increase to
239 leaves over the 200 days.
14
Note: Corn or maize takes 60 to 90 days to grow depending on the variety, (Mierzeje wski [41], so stopping the
simu lation experiment within a 100 day period is in line with this. Ho wever, to prove the stability of the system, the graph of
Fig. 15. shows that a stable system is obtained when wasps established their abode in an ecosystem as shown when the
simulation experiment was extended to 200 days .
The result of Fig.15 shows that the system is stable, as the deployment of the larvae wasps alone had provided
reasonable control without the deployment of chemical pesticides.
500
Se eggs
Se larvae
Cotesia F.
Se pupae
Se adults
Maize leaves
450
400
350
300
250
200
150
Maize Leaf(Nlf)
100
50
0
0
20
40
60
80
100
120
140
160
180
200
Time (days)
Fig. 15 System stability and equilibrium
6.
Results analysis
In Fig. 9, there is uninterrupted leaf growth of the maize crop over a 100 day period; due to the absence of pests the
leaf population density increased from 68 leaves/m2 to 281 leaves/m2 with in a 100 day period. Once the Se moth enters the
environment they lay eggs, which hatch into larva, which eat the leaves and the leaf growth is attenuated. This is illustrated
in Fig. 10, wh ich also shows that there is a rapid increase in larva population density that attenuates as the food supply
becomes inadequate to support the over population. In Fig. 11, the leaf population dropped slightly after 34 days, as the
density of Cotesia Flavipes (Cameron) larval parasitoid wasps introduced was inadequate to suppress the rapidly growing
pest population; Fig. 13 also illustrates the destructive nature of caterp illars in the established infestation of Fig. 14 and Fig.
15 shows the control effect when the right quantity of wasps is deployed into the habitat, the pest outbreak will be min imized
and food security will be achieved as the relationship between the pest, wasps and the habitat becomes symbiotic.
Via systematic application of the nu merical model it has been demonstrated that it is possible to optimize biological
pest control strategy. The model demonstrates the symbiotic existence, at a sustainable level, of the parasitoid wasps and the
caterpillar over a longer period as demonstrated in Figs. 12 and 14. Clearly we do not want to completely eradicate the larvae
population because the absence of larvae means that the wasps cannot survive, as shown in Figs. 11 & 12 , when the wasps
were deployed in the absence of an initial larvae starting population, the wasp population dropped fro m 5 to 2 and 25 to 6 in
the first 16 and 23 days, respectively. This pest control planning tool provides agriculturists with the means to calculate the
number of parasitoid wasps to deploy in any pest infested environ ment and the timing schedule in order to suppress the pest
population. This approach offers a replacement for pesticides to enable the quality of life for all of hu mankind to be
improved by using parasitoid wasps for the sustainable control of pests. For rapid response to a n established pest infestation,
this study illustrates that it is advantageous to deploy a sizable nu mber of Cotesia Flavipes (Cameron) larval parasitoid
wasps as illustrated in Fig. 12, and Fig. 14. When a sizable number of larval wasps were introduced leaf growth was
sustained, the population of the larvae was under control within a short time and the lea f destruction was less over the first
15
few days as well as the population density of the caterpillars being drastically reduced by the Cotesia Flavipes Cameron
larval parasitoid wasps. This pest control approach will result in the production of a high quality of crops with good yield.
Furthermore, there will be a great reduction in health hazard and difficulties triggered from contact with the caterpillar.
7.
Conclusion
This research contributes to the development of an autonomous integrated pest management system fo r farmers across
the globe with the goal of discouraging comp letely the application o f conventional pesticides that pose risks to our
immed iate environment and human health. The research demonstrates the use of Cotesia Flavipes (Cameron) larval
parasitoids wasps (natural enemies) to control and eradicate the problems resulting from the spread of caterpillars in our
environment and farms without actually coming into direct contact with any of the species whether stingers or non -stingers,
our interest is to make the environ ment friendly and habitable. We have created a strategy for managing the caterpillar pest
generally by deploying a proposed parasitoid delivery system that deploys wasps based on statistical evaluation of the
problem; this tool will enable farmers or gro wers to know exactly the nu mber of Cotesia Flavipes (Cameron) larval
parasitoid wasps to deploy and the period it will take to achieve control of t he pest. These results agree with the initial
findings of Faithpraise [11] wh ich proves that larval parasitoids are capable of managing a pest infested habitat without the
application of pesticides despite the continuous reproduction of the adult stage. Future work will investigate the possibility of
combin ing egg and larval parasitoid wasps stages to observe the effect on the field and to achieve greater crop productivity
in terms of food quality and quantity for economic benefit.
References
[1] C. A. Frazier and F. K. Brown. “Insects and allergy and what to do about them,” Un iversity of Oklahoma Press. Norman,
Oklahoma, 1980.
[2] L. L. Hyche “Stinging Caterpillars a Guide to Recognition of Species Found on Alabama Trees ”
http://www.ag.auburn.edu/enpl/bulletins/caterpillar/caterpillar.htm, February 1998.
[3] C. S. M. Malaque, L. C. Andrade, G. Madalosso, S. Tomy, F. L. Tavares, and A. C. Seguro , “A Case Of Hemolysis
Resulting from contact with a Lonomia Caterpillar In Southern Brazil,” Am. J. Trop. Med. Hyg., vol.74, no.5, 2006, pp.
807–809.
[4] C. L Arocha-Piñango, “Fibrinolysis caused by contact with caterpillars: preliminary communication,” Acta Cient Venez
vol.18, 1996, pp.136–139.
[5] E. M Kelen, Z. P. Picarelli and A. C. Duarte, “Hemorrhagic syndrome induced by contact with caterpillars of the genus
Lonomia (Saturniidae, Hemileucinae),” J Toxicol Toxin Rev, vol.14, 1996, pp.283–308.
[6] H. R. Brady, B. M. Brenner and W. Lieberthal, “Acute renal failure,” Brenner BM, ed. The Kidney. Fifth edition.
Philadelphia: W. B. Saunders Company, 1996, pp.1200–1252.
[7] E. A. Burdmann, I. Antune, L. B. Saldanha and R.C. Abdulkader, “Severe acute renal failure induced by the venom of
Lonomia caterpillars,” Clin Nephrol vol.46, 1996, pp.337–339.
[8] G.P. Chikwenhere, “Status of armyworm outbreaks and control in Zimbabwe,” NewsdzeZimbabwe . January 06, 2013
http://www.newsdzezimbabwe.co.uk/2013/01/army worm-outbreak-hits- zimbabwe.html
[9] S.Wakamura, H.Yasu, M. Shimatani, T. Tokashiki, T. Okuhira, N. Fujiwara-Tsujii, M. Ishikawa, T. Uesato, H.
Zukeyama and H. Tobaru, “Sex pheromone of the African armyworm Spodoptera exempta (Lepidoptera: Noctuidae):
identification of components of the Okinawan population and formulation for population monitoring,” Appl Entomol
Zool vol.46, 1996, pp.415–420.
[10] F. Faithpraise, P. Birch, R. Young, J. Obu, B. Faithpraise and C. Chatwin, “Automatic plant pest detection &
recognition using k-means clustering algorithm & correspondence filters”, International Journal of Advanced
Biotechnology and Research, Vol. 4, Issue 2, 2013, pp. 1052-1062.
[11] I. Kypraios, R. Young, P. Birch and C. Chatwin, “Object recognition within cluttered scenes employing a hybrid optical
neural network filter,” Optical Engineering vol.43, no.8, 2004, pp.1839-1850.
16
[12] F. Faithpraise, C. Chatwin, J. Obu, R. Young and P. Birch (2013a), “Targeting the life cycle stages of the Diamond
Back Moth (Plutella Xylostella) with three different parasitoid wasps,” Technical Report, Sussex University,
TR/SU/FF/131202, 02 Nov. 2013, pp.1-18
[13] F. Faithpraise, C. R. Chatwin, R. C. D. Young and P.M. Birch, (2013b) “Timely Control of Aphis craccivora Using an
automatic robotic drone management system (ARDMS),” Technical Report, TR/SU/FF/130617, 17 June 2013, pp.1-19
[14] H. C. J. Godfray, “Parasitoids: behavioural and evolutionary ecology,” USA, ISBN 0-691-03325-0, 1996, pp.1-473.
[15] R.O.C E. Yuan, “Exhibition of Agricultural Science and Technology,” ©2011 Co uncil of Agriculture, 2011
http://flora.coa.gov.tw/view_eng.php?id=250 (Retrieved 06/03/13)
[16] K. M. Edson, S.B. Vinson, D. B. Stoltz, and M. D. Summers, “Virus in a parasitoid wasp: Suppression of t he cellular
immune response in the parasitoid's host,” Science, 211, 1981, pp.582-583.
[17] A. H. Grosman , A. Janssen, E. F.de Brito, E.G. Cordeiro and F. Colares, “Parasitoid Increases Survival of Its Pupae by
Inducing Hosts to Fight Predators,” PLoS ONE 3(6): e2276. doi:10.1371/journal.pone.0002276, 2008
[18] F. O. Faithpraise, J. Idung, B. Usibe, C. R. Chatwin, R.Young and P.Birch, “Natural control of the mosquito population
via Odonata and Toxorhynchites,” International Journal of Innovative Research in Science, Engineering and
Technology, ISSN: 2319-8753, Vol.3, Issue 5, 12898-12911, May 2014
[19] R. Kfir, W. A. Overholt, Z. R. Khan, and A. Polaszek, “Biology and management of economically important
lepidopteran cereal stem borers in Africa,” Annual Review of Entomology. Vol.47, 2002, pp.701-731.
[20] R. P. J. Potting,“Hunting for hiding hosts: the behavioral ecology of the stemborer parasitoid Cotesia Flavipes
(Cameron),” - 111. Thesis Wageningen. - With ref. - With summary in Dutch. ISBN 90-5485-551-7, 1996.
[21] Murthy K. Srinivasa and Rajeshwari R., “Host Searching Efficiency of Cotesia Flavipes (Cameron) Cameron
(Hymenoptera: Braconidae) an Important Parasitoid of the Maize Stem Borer Chilo Partellus Swinhoe,” Indian Journal
of Fundamental and Applied Life Sciences ISSN: 2231-6345. http://www.cibtech.org/jls.htm vol.1, no.3 July-September,
2011, pp.71-74.
[22] R. Tanwar, and A. Varma, “Field trials with Cotesia Flavipes (Cameron) Cameron against Sugar Cane Borers in subTropical India,” Short Communication vol. 4, no.3, 2002 , pp.153-156.
[23] E. I. Niyibigira, “Genetic variability in Cotesia Flavipes (Cameron) and its importance in biological control of
lepidopteran stem borers,” PhD Thesis, Wageningen University, 162pp, 2003.
[24] P. Mangano,“Armyworm in Cereal Crops,” Entomology Branch, Department of Agriculture and Food, South Perth.
Replaces Farmnote 110/84, 1804-08/06-ID6567 ISSN 0726-934X site www.agric.wa.gov.au
[25] P. Glogoza, “The Armyworm and the Army Cutworm. North Dakota State University Ag riculture and University
Extension,” 2000. http://www.ag.ndsu.edu/pubs/plantsci/pests/ e830w.htm (retrieved 23 /02/ 2014).
[26] J. Holt, W. Mushobozi, R.K. Day, J.D. Knight, M. Kimani, J. Njuki and R. Musebe., “A simple Bayesian network to
interpret the accuracy of armyworm outbreak forecasts,” Annals of Applied Biology vol.148, 2006 , pp.141-6.
[27] A.M.A. Mahmoud, E. J. de Luna-Santillana and M. A. Rodríguez-Perez, “Parasitism by the endoparasitoid, Cotesia
Flavipes (Cameron) induces cellular immunosuppression and enhances susceptibility of the sugar cane borer, Diatraea
saccharalis to Bacillus thuringiensis” Journal of Insect Science 11:119 available online: insectscience.org/11.119, 2011.
[28] W. A. Hoffmann and H. Poorter, “Avoiding Bias in Calculations of Relative Growth Rate.,” Annals of Botany vol.90,
no.37, 2002. doi:10.1093/aob/mcf140
[29] V. R. Tóth, I. Mészáros, S. J. Palmer, S. z. Veres and I. Précsényi, “Nitrogen Deprivation Induces Changes in the Leaf
Elongation Zone of Maize Seedlings,” Biologia Plantaru m , vol.45, no.2, 2002, pp.241-247.
[30] H. Nelissen, B. Rymen, F. Coppens, S. Dhondt, F. Fiorani, and G. S. Beemster, “Kinematic analysis of cell division in
leaves of mono- and dicotyledonous species: A basis for understanding growth and developing refined molecular
sampling strategies”. In Plant Organogenesis (Methods in Molecular Biology vol. 959, I. De Smet (ed.), New York,
Humana Press, 2013, pp. 247-264.
[31] A. K. Hardacre, and H. L. Turnbull, “The Growth and Development of Maize (Zea mays L.) at Five Temperatures,”
Annals of Botany vol.58, 1986, pp779-787.
[32] F. E. Klassische, “Runge-Kutta-Formeln fünfter and siebenter Ordnung mit Schrittweiten-Kontrolle“, Computing (Arch.
Elektron. Rechnen) 4 , 1969, pp. 93-106.
[33] J.R. Dormand, and P. J. Prince,“High order embedded Runge-Kutta formulae,” J. Comput. Appl. Math. 7 (1981), no.1,
1981, pp.67-75.
[34] J. Butcher,“ Runge-Kutta methods,” Scholarpedia, vol.2, no.9, 2007, pp.3147.
[35] R. Schreiber, “MATLAB,” Scholarpedia, vol.2, no.7, 2007, pp.2929.
17
[36] C. Chatfield, “statistics for technology: a course in applied statistics,” third edition (Revised) Chapman & Hall, London,
ISBN 0-412-25340-2: pp. 327-330, 1992.
[37] B. Ostle, V. K. Turner, Jr., C.R. Hicks, & G. W. McElrath , “Engineering Statistics, the Industrial Experience,”
Duxbury Press, United State of America, ISBN 0-534-26538-3: pp.161-165
[38] F. Faithpraise, C. Chatwin, J. Obu, R. Young and P. Birch, “Pattern Recognition on Natural Control of the population
density of mosquito species via Odonata and Toxorhynchites,” Technical Report, University of Sussex,
TR/SU/FF/131102, 02 Nov. 2013, pp.1-18
[39] F. Faithpraise, C. Chatwin, J. Obu , B. Olawale, R. Young and P. Birch, “Sustainable Control of Anopheles Mosquito
Population,” Environment, Ecology & Management, vol.3, no.1, Feb. 2014, pp.1-19.
[40] B. Bean and P. Carl “Corn Development and Key Growth Stages”, Texas A&M. Agricultural Research & Extension
Centre Amarillo. osti.gov › eprints › topicpages (Retrieved 25/02/14)
[41] K. Mierzejewski (02/05/2014). “How long does it take for corn to grow?” http://www.gardeningknowhow.com/
edible/vegetables/corn/how-to-grow-corn.htm (Retrirved 20/05/2014)
Dedication
We dedicate this paper to memory of Dr M ing-Yaw Huang who co mpleted h is Doctorate with us in the Un ited
Kingdom in 1994 and subsequently worked in Taiwan as a highly innovative and adventurous Engineer.

- Sussex Research Online

Transcription

Similar documents

Cotesia flavipes in borer control

PrimeFilm 1800 AFL Low-Cost 35mm Slide/Film Scanning Through

Статья

3. mantenimiento de equipos

Great Graffiti from Great Graffiti from Group 6

Controlling Wasps, Bees and Hornets Around Your Home

2007-2008 Ducati Monster S4R/S

march - Cobblestone Apartments

Attached File

SCIENTIFIC NOTE

News from Room 3 - Bankwood School

Click here - Graduate Training in Risk Analysis for Introduced